"Near Data" and "Far Data" for Urban Sustainability: How Do Community Advocates Envision Data Intermediaries?
Bibliographic record
Abstract
In the densifying data ecosystem of today's cities, data intermediaries are crucial stakeholders in facilitating data access and use. Community advocates live in these sites of social injustices and opportunities for change. Highly experienced in working with data to enact change, they offer distinctive insights on data practices and tools. This paper examines the unique perspectives that community advocates offer on data intermediaries. Based on interviews with 17 advocates working with 23 grassroots and nonprofit organizations, we propose the quality of "near" and "far" to be seriously considered in data intermediaries' works and articulate advocates' vision of connecting "near data" and "far data." To pursue this vision, we identified three pathways for data intermediaries: align data exploration with ways of storytelling, communicate context and uncertainties, and decenter artifacts for relationship building. These pathways help data intermediaries to put data feminism into practice, surface design opportunities and tensions, and raise key questions for supporting the pursuit of the Right to the City.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.021 | 0.036 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".